Geometric Relations Between Rigid Bodies (Part 1): Semantics for Standardization

Abstract. The design, simulation and programming of robotics systems is challenging as expertise from multiple domains needs to be integrated conceptually and technically. Domain-specific modeling promises an efficient and flexible concept for developing robotics applications that copes with this challenge. It allows to raise the level of abstraction through the use of specific concepts that are closer to the respective domain concerns and easier to understand and validate. Furthermore, it focuses on increasing the level of automation, e.g. through code generation, to bridge the gap between the modeling and the implementation levels and to improve the efficiency and quality of the software development process. Within this contribution, we survey the literature available on domain-specific (modeling) languages in robotics required to realize a state-of-the-art real-world example from the RoboCup@Work competition. We classify 41 publications in the field as reference for potential DSL users. Furthermore, we analyze these contributions from a DSL-engineering viewpoint and discuss quantitative and qualitative aspects such as the methods and tools used for DSL implementation as well as their documentation status and platform integration. Finally, we conclude with some recommendations for discussion in the robotics programming and simulation community based on the insights gained with this survey.

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“…Laet et al propose their semantics for standardization in the context of the robotics engineering task force [22].…”

Section: Discussionmentioning

confidence: 99%

A Survey on Domain-Specific Languages in Robotics

Nordmann

Hochgeschwender

Wrede

2014

Lecture Notes in Computer Science

101

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“…The invariants for translation ( 1 , 2 and 3 ) are defined and expressed in a moving frame, referred to as the translational Frenet-Serret frame { }. This frame is an orientation frame [15], i.e its position in space does not matter, but for visualization purposes the origin is often set to coincide with the point's location, as in Figure 4b. The first axis of this frame is the tangent to the point trajectory, the second axis lies in the direction along which the tangent is instantaneously rotating, and the third axis follows from the first two axes through orthogonality.…”

Section: Object Translationmentioning

confidence: 99%

Generalizing demonstrated motion trajectories using coordinate-free shape descriptors

Vochten

Laet

Schutter

2019

Robotics and Autonomous Systems

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In learning by demonstration, the generalization of motion trajectories far away from the set of demonstrations is often limited by the dependency of the learned models on arbitrary coordinate references. Trajectory shape descriptors have the potential to remove these dependencies by representing demonstrated trajectories in a coordinate-free way. This paper proposes a constraint-based optimization framework to generalize demonstrated rigid-body motion trajectories to new situations starting from the shape descriptor of the demonstration. Experimental results indicate excellent generalization capabilities showing how, starting from only a single demonstration, new trajectories are easily generalized to novel situations anywhere in task space, such as new initial or target positions and orientations, while preserving similarity with the demonstration. The results encourage the use of trajectory shape descriptors in learning by demonstration to reduce the number of required demonstrations.

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“…Here, many approaches use the local arrangement of individual object parts with respect to each other for object recognition [7], [6]. But besides these local contextual cues describing the object, its global, coordinate invariant geometric relations with other objects provide additional important clues for its recognition [8]. Graphical models provide a unified framework that allows incorporating both the object's local and global features and have for this reason been widely used for this task [5], [9], [10].…”

Section: Introductionmentioning

confidence: 99%

Recognizing Point Clouds Using Conditional Random Fields

Husain

Dellen

Torras

2014

2014 22nd International Conference on Pattern Recognition

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Abstract-Detecting objects in cluttered scenes is a necessary step for many robotic tasks and facilitates the interaction of the robot with its environment. Because of the availability of efficient 3D sensing devices as the Kinect, methods for the recognition of objects in 3D point clouds have gained importance during the last years. In this paper, we propose a new supervised learning approach for the recognition of objects from 3D point clouds using Conditional Random Fields, a type of discriminative, undirected probabilistic graphical model. The various features and contextual relations of the objects are described by the potential functions in the graph. Our method allows for learning and inference from unorganized point clouds of arbitrary sizes and shows significant benefit in terms of computational speed during prediction when compared to a state-of-the-art approach based on constrained optimization.

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Geometric Relations Between Rigid Bodies (Part 1): Semantics for Standardization

Cited by 25 publications

References 6 publications

A Survey on Domain-Specific Languages in Robotics

A Survey on Domain-Specific Languages in Robotics

Generalizing demonstrated motion trajectories using coordinate-free shape descriptors

Recognizing Point Clouds Using Conditional Random Fields

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